Building decision trees for characteristic ellipsoid method to monitor power system transient behaviors

This paper presents the idea and initial results of building decision trees (DTs) for detecting and identifying various transient dynamic events using the characteristic ellipsoid method. In this paper, the objective is to determine fault types, fault locations and clearance times in the system using DTs based on ellipsoids surrounding system transient responses in the system operating parameter space. The New England 10-machine 39-bus system is used to generate a sufficiently large number of transient events in different system configurations. Comprehensive transient simulations considering three fault types, two different fault clearance times and various fault locations were conducted in the study. Bus voltage magnitudes and monitored reactive and active power flows are recorded as the phasor measurements to calculate characteristic ellipsoids whose volume, eccentricity, center and projection of the longest axis on the parameter space coordinates are used as indices to build decision trees. The DTs performance is tested and compared for different sets of phasor measurement units (PMUs) locations. The results demonstrate that, depending on the number and location of PMUs in the model, the proposed approach is capable to detect the fault type, location, and clearance time in up to 99% of the cases which are not included in the training set used to build the DT.

[1]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[2]  J. Ross Quinlan,et al.  Decision trees and decision-making , 1990, IEEE Trans. Syst. Man Cybern..

[3]  P. Utgoff,et al.  Multivariate Decision Trees , 1995, Machine Learning.

[4]  N. Senroy,et al.  Decision Tree Assisted Controlled Islanding , 2006, IEEE Transactions on Power Systems.

[5]  Jian Ma,et al.  Use multi-dimensional ellipsoid to monitor dynamic behavior of power systems based on PMU measurement , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[6]  Nikos D. Hatziargyriou,et al.  Investigation of Decision Trees (DTs) Parameters for Power System Voltage Stability Enhancement , 2006, SETN.

[7]  N.D. Hatziargyriou,et al.  Decision trees for determination of optimal location and rate of series compensation to increase power system loading margin , 2006, IEEE Transactions on Power Systems.

[8]  Jian Ma,et al.  Characteristic ellipsoid method for monitoring power system dynamic behavior using phasor measurements , 2007, 2007 iREP Symposium - Bulk Power System Dynamics and Control - VII. Revitalizing Operational Reliability.

[9]  S. P. Teeuwsen,et al.  Genetic algorithm and decision tree based oscillatory stability assessment , 2006, 2005 IEEE Russia Power Tech.

[10]  Y. Sheng,et al.  Decision Trees and Wavelet Analysis for Power Transformer Protection , 2002, IEEE Power Engineering Review.

[11]  C. S. Wallace,et al.  Coding Decision Trees , 1993, Machine Learning.

[12]  Piyush Kumar,et al.  Minimum-Volume Enclosing Ellipsoids and Core Sets , 2005 .

[13]  Stavros A. Papathanassiou,et al.  Decision trees for fast security assessment of autonomous power systems with a large penetration from renewables , 1995 .

[14]  Louis Wehenkel,et al.  Decision tree based transient stability method a case study , 1994 .

[15]  C. W. Taylor,et al.  Decision trees using apparent resistance to detect impending loss of synchronism , 2000 .

[16]  Hong-Tzer Yang,et al.  Power system distributed on-line fault section estimation using decision tree based neural nets approach , 1995 .

[17]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

[18]  Nikos D. Hatziargyriou,et al.  A decision tree method for on-line steady state security assessment , 1994 .

[19]  Sreerama K. Murthy,et al.  Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey , 1998, Data Mining and Knowledge Discovery.

[20]  James S. Thorp,et al.  Decision trees for real-time transient stability prediction , 1994 .

[21]  Kyuseok Shim,et al.  Building Decision Trees with Constraints , 2001 .

[22]  M. Begovic,et al.  Nondominated sorting genetic algorithm for optimal phasor measurement placement , 2002, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[23]  L. Rouco,et al.  Decision trees applied to the management of voltage constraints in the Spanish market , 2005, IEEE Transactions on Power Systems.

[24]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[25]  Chien-Chun Yang,et al.  Estimation of line flows and bus voltages using decision trees , 1994 .

[26]  M. Begovic,et al.  Nondominated Sorting Genetic Algorithm for Optimal Phasor Maesurement Placement , 2002 .

[27]  S.M. Rovnyak,et al.  Response-based decision trees to trigger one-shot stabilizing control , 2004, IEEE Transactions on Power Systems.

[28]  Louis Wehenkel,et al.  Decision trees and transient stability of electric power systems , 1991, Autom..

[29]  N. D. Hatziargyriou,et al.  On-Line Preventive Dynamic Security of Isolated Power Systems Using Decision Trees , 2002, IEEE Power Engineering Review.

[30]  Vijay Vittal,et al.  An Online Dynamic Security Assessment Scheme Using Phasor Measurements and Decision Trees , 2007 .

[31]  Ruisheng Diao,et al.  Decision Tree-Based Online Voltage Security Assessment Using PMU Measurements , 2009, IEEE Transactions on Power Systems.